Expected posterior priors for model comparison in a class of discrete graphical models

This paper develops a procedure based on Expected Posterior Priors to perform Bayesian model comparison for discrete undirected decomposable graphical models. The basic idea is that priors should not be assigned separately under each model; rather they should be related across models, in order to acquire some degree of compatibility, and thus allow fairer and more robust comparisons. The methodology is illustrated through the analysis of a 2 x 3 x 4 contingency table.